ThyroidNet: A Deep Learning Network for Localization and Classification of Thyroid Nodules.

IF 2.2 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-12-30 DOI:10.32604/cmes.2023.031229
Lu Chen, Huaqiang Chen, Zhikai Pan, Sheng Xu, Guangsheng Lai, Shuwen Chen, Shuihua Wang, Xiaodong Gu, Yudong Zhang
{"title":"ThyroidNet: A Deep Learning Network for Localization and Classification of Thyroid Nodules.","authors":"Lu Chen, Huaqiang Chen, Zhikai Pan, Sheng Xu, Guangsheng Lai, Shuwen Chen, Shuihua Wang, Xiaodong Gu, Yudong Zhang","doi":"10.32604/cmes.2023.031229","DOIUrl":null,"url":null,"abstract":"<p><strong>Aim: </strong>This study aims to establish an artificial intelligence model, ThyroidNet, to diagnose thyroid nodules using deep learning techniques accurately.</p><p><strong>Methods: </strong>A novel method, ThyroidNet, is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules. First, we propose the multitask TransUnet, which combines the TransUnet encoder and decoder with multitask learning. Second, we propose the DualLoss function, tailored to the thyroid nodule localization and classification tasks. It balances the learning of the localization and classification tasks to help improve the model's generalization ability. Third, we introduce strategies for augmenting the data. Finally, we submit a novel deep learning model, ThyroidNet, to accurately detect thyroid nodules.</p><p><strong>Results: </strong>ThyroidNet was evaluated on private datasets and was comparable to other existing methods, including U-Net and TransUnet. Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules. It achieved improved accuracy of 3.9% and 1.5%, respectively.</p><p><strong>Conclusion: </strong>ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks. Future research directions include optimization of the model structure, expansion of the dataset size, reduction of computational complexity and memory requirements, and exploration of additional applications of ThyroidNet in medical image analysis.</p>","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2023-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615790/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cmes-computer Modeling in Engineering & Sciences","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.32604/cmes.2023.031229","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Aim: This study aims to establish an artificial intelligence model, ThyroidNet, to diagnose thyroid nodules using deep learning techniques accurately.

Methods: A novel method, ThyroidNet, is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules. First, we propose the multitask TransUnet, which combines the TransUnet encoder and decoder with multitask learning. Second, we propose the DualLoss function, tailored to the thyroid nodule localization and classification tasks. It balances the learning of the localization and classification tasks to help improve the model's generalization ability. Third, we introduce strategies for augmenting the data. Finally, we submit a novel deep learning model, ThyroidNet, to accurately detect thyroid nodules.

Results: ThyroidNet was evaluated on private datasets and was comparable to other existing methods, including U-Net and TransUnet. Experimental results show that ThyroidNet outperformed these methods in localizing and classifying thyroid nodules. It achieved improved accuracy of 3.9% and 1.5%, respectively.

Conclusion: ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis tasks. Future research directions include optimization of the model structure, expansion of the dataset size, reduction of computational complexity and memory requirements, and exploration of additional applications of ThyroidNet in medical image analysis.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
甲状腺网络:用于甲状腺结节定位和分类的深度学习网络
目的:本研究旨在建立一个人工智能模型 ThyroidNet,利用深度学习技术准确诊断甲状腺结节:方法:介绍并评估一种基于深度学习的新方法 ThyroidNet,用于甲状腺结节的定位和分类。首先,我们提出了多任务 TransUnet,它将 TransUnet 编码器和解码器与多任务学习相结合。其次,我们提出了针对甲状腺结节定位和分类任务的 DualLoss 函数。它平衡了定位和分类任务的学习,有助于提高模型的泛化能力。第三,我们介绍了增强数据的策略。最后,我们提交了一个新颖的深度学习模型 ThyroidNet,用于准确检测甲状腺结节:我们在私人数据集上对 ThyroidNet 进行了评估,结果与 U-Net 和 TransUnet 等其他现有方法不相上下。实验结果表明,ThyroidNet 在甲状腺结节的定位和分类方面优于这些方法。结论:结论:ThyroidNet 能明显改善甲状腺结节的临床诊断,并支持医学图像分析任务。未来的研究方向包括优化模型结构、扩大数据集规模、降低计算复杂度和内存要求,以及探索 ThyroidNet 在医学图像分析中的其他应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Cmes-computer Modeling in Engineering & Sciences
Cmes-computer Modeling in Engineering & Sciences ENGINEERING, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
CiteScore
3.80
自引率
16.70%
发文量
298
审稿时长
7.8 months
期刊介绍: This journal publishes original research papers of reasonable permanent value, in the areas of computational mechanics, computational physics, computational chemistry, and computational biology, pertinent to solids, fluids, gases, biomaterials, and other continua. Various length scales (quantum, nano, micro, meso, and macro), and various time scales ( picoseconds to hours) are of interest. Papers which deal with multi-physics problems, as well as those which deal with the interfaces of mechanics, chemistry, and biology, are particularly encouraged. New computational approaches, and more efficient algorithms, which eventually make near-real-time computations possible, are welcome. Original papers dealing with new methods such as meshless methods, and mesh-reduction methods are sought.
期刊最新文献
ThyroidNet: A Deep Learning Network for Localization and Classification of Thyroid Nodules. A Survey on Artificial Intelligence in Posture Recognition. A Survey of Convolutional Neural Network in Breast Cancer. A Novel SE-CNN Attention Architecture for sEMG-Based Hand Gesture Recognition ER-Net: Efficient Recalibration Network for Multi-View Multi-Person 3D Pose Estimation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1